[09:25] <rmg51> Morning
[11:53] <icey> ahoy
[12:21] <teddy-dbear> Morning peoples, critters and everything else
[13:50] <MutantTurkey> waltman: got a minute for a math question?
[13:51] <MutantTurkey> is there a mathematical way to represent this "weighted averages of averages": for all the elements in N array, containing two fields, average and total, (Navg[0] x Ntotal[0]) + (Navg[1] x Ntotal[1]) + ... etc  all divided by total sum of the Ntotal column
[14:28] <waltman> Sorry, I'm confused about what you want to do, and also by what you mean by "a mathematical way".
[14:30] <waltman> Since it sounds like you know what calculations you want to do, can't you just do them?
[17:01] <MutantTurkey> waltman: no i wrote the script already, i didn't know if there was a more technical term for what i did
[17:01] <MutantTurkey> one that was already existing i didn't know about
[17:02] <MutantTurkey> waltman: not sure if math people have a term for averaging averages
[17:17] <waltman> Well, for one thing, it wasn't even super clear that's what you were doing.
[17:17] <waltman> Also, unless those averages are all of the same size, I'm not sure it even makes mathematical sense.
[20:20] <MutantTurkey> waltman: they're not the same size, but i am trying to come up with a closish approximation, by weighting them against how many that avg represents
[20:22] <MutantTurkey> waltman: essentially, we have a cached number of reviews and their average for each category on our website. On a page that contains many categories, my designer wants an overall average. which, would kill performance, so i want to leverage that cach'd average
[20:26] <MutantTurkey> using just the average itself was giving wonky results, with 3 categorys, Cat A -> avg 1/5 stars, total reviews 1, Cat B avg 4.5starts total reviews 20, Cat C, avg 5.0stars  total reviews 50, just averaging those averages raw gives me ~3.5, when most of the reviews have been very positive, with outliers causing more issues,
[20:27] <MutantTurkey> by normalizing the average somewhat, (1 star * 1 review) + (4.5 star * 20 reviews) + (5.0 star * 50 reviews) / (71 reviews total) = 4.8 stars, somewhat more representative of the reviews given